Manifold-based Shapley for SAR Recognization Network Explanation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916081865588736 |
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| author | Hu, Xuran Zhu, Mingzhe Liu, Yuanjing Feng, Zhenpeng Stankovic, LJubisa |
| author_facet | Hu, Xuran Zhu, Mingzhe Liu, Yuanjing Feng, Zhenpeng Stankovic, LJubisa |
| contents | Explainable artificial intelligence (XAI) holds immense significance in enhancing the deep neural network's transparency and credibility, particularly in some risky and high-cost scenarios, like synthetic aperture radar (SAR). Shapley is a game-based explanation technique with robust mathematical foundations. However, Shapley assumes that model's features are independent, rendering Shapley explanation invalid for high dimensional models. This study introduces a manifold-based Shapley method by projecting high-dimensional features into low-dimensional manifold features and subsequently obtaining Fusion-Shap, which aims at (1) addressing the issue of erroneous explanations encountered by traditional Shap; (2) resolving the challenge of interpretability that traditional Shap faces in complex scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03128 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Manifold-based Shapley for SAR Recognization Network Explanation Hu, Xuran Zhu, Mingzhe Liu, Yuanjing Feng, Zhenpeng Stankovic, LJubisa Artificial Intelligence H.1.m Explainable artificial intelligence (XAI) holds immense significance in enhancing the deep neural network's transparency and credibility, particularly in some risky and high-cost scenarios, like synthetic aperture radar (SAR). Shapley is a game-based explanation technique with robust mathematical foundations. However, Shapley assumes that model's features are independent, rendering Shapley explanation invalid for high dimensional models. This study introduces a manifold-based Shapley method by projecting high-dimensional features into low-dimensional manifold features and subsequently obtaining Fusion-Shap, which aims at (1) addressing the issue of erroneous explanations encountered by traditional Shap; (2) resolving the challenge of interpretability that traditional Shap faces in complex scenarios. |
| title | Manifold-based Shapley for SAR Recognization Network Explanation |
| topic | Artificial Intelligence H.1.m |
| url | https://arxiv.org/abs/2401.03128 |